Effect of implementing infection control guidelines on minimizing surgical wound infection for patients undergoing neurosurgery
Bibliographic record
Abstract
Background and objective: Nursing role delivered at all levels of care is critical to eliminate surgical wound infection after neurosurgery. Aim: Evaluate the effect of implementing infection control guidelines on minimizing surgical wound infection for patients undergoing neurosurgery.Methods: Pretest/posttest was used to assess and evaluate nurses' practices pre and post implementation of infection control guidelines while posttest only was used for patients to evaluate the effect of implementing infection control guidelines on minimizing surgical wound infection. Thirty-six nurses in neurosurgery department at Assiut Neurological, Psychiatric and Neurosurgery University Hospital, also 443 patients undergoing neurosurgery were included. Nurses' practices were assessed pre and post implementation of infection control guidelines. Tools: Nurses' observation checklist, patients’ assessment sheet, patients’ follow up sheet and neurosurgery infection control guidelines (teaching booklet) for nurses. Results: Nurses’ practices were improved, infection in neurosurgery was eradicated according to results of environmental swabs and surgical wound infection was reduced post implementation of infection control guidelines.Conclusion and recommendation: Proper implementation of disinfection and sterilization enhance safe and effective care. Infection control policy should be developed in neurosurgery department and all healthcare team should be responsible to adhere and implement it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".